Recent publications
Article
Goodman, T, Van Gemst, K & Tino, P 2021, 'A geometric framework for pitch estimation on acoustic musical signals', The Journal of Mathematics and Music.
Zhang, S, Tino, P & Yao, X 2021, 'Hierarchical reduced-space drift detection framework for multivariate supervised data streams', IEEE Transactions on Knowledge and Data Engineering.
Verzelli, P, Alippi, C, Livi, L & Tino, P 2021, 'Input-to-state representation in linear reservoirs dynamics', IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3059389
Canducci, M, Tino, P & Mastropietro, M 2022, 'Probabilistic modelling of general noisy multi-manifold data sets', Artificial Intelligence, vol. 302, 103579. https://doi.org/10.1016/j.artint.2021.103579
Akerman, I, Kasaai, B, Bazarova, A, Sang, PB, Peiffer, I, Artufel, M, Derelle, R, Smith, G, Rodriguez-Martinez, M, Romano, M, Kinet, S, Tino, P, Theillet, C, Taylor, N, Ballester, B & Méchali, M 2020, 'A predictable conserved DNA base composition signature defines human core DNA replication origins', Nature Communications, vol. 11, no. 1, 4826 . https://doi.org/10.1038/s41467-020-18527-0
Gokhale, KM, Chandan, JS, Toulis, K, Gkoutos, G, Tino, P & Nirantharakumar, K 2020, 'Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies', European Journal of Epidemiology. https://doi.org/10.1007/s10654-020-00677-6
Tino, P 2020, 'Dynamical systems as temporal feature spaces', Journal of Machine Learning Research, vol. 21, no. 44, 19-589, pp. 1-42. <http://jmlr.org/papers/v21/19-589.html>
Pfannschmidt, L, Jacob, J, Hinder, F, Biehl, M, Tino, P & Hammer, B 2020, 'Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information', Neurocomputing. https://doi.org/10.1016/j.neucom.2019.12.133
Tang, F, Fan, M & Tino, P 2020, 'Generalized Learning Riemannian Space Quantization: a Case Study on Riemannian Manifold of SPD Matrices', IEEE Transactions on Neural Networks and Learning Systems.
Alzheimer’s Disease Neuroimaging Initiative, Giorgio, J, Landau, S, Jagust, W, Tino, P & Kourtzi, Z 2020, 'Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease', NeuroImage: Clinical, vol. 26, 102199, pp. 1-14. https://doi.org/10.1016/j.nicl.2020.102199
Pauli, R, Tino, P, Rogers, JC, Baker, R, Clanton, R, Birch, P, Brown, A, Daniel, G, Ferreira, L, Grisley, L, Kohls, G, Baumann, S, Bernhard, A, Martinelli, A, Ackermann, K, Lazaratou, H, Tsiakoulia, F, Bali, P, Oldenhof, H, Jansen, L, Smaragdi, A, Gonzalez-Madruga, K, Gonzalez-Torres, MA, González de Artaza-Lavesa, M, Steppan, M, Vriends, N, Bigorra, A, Siklósi, R, Ghosh, S, Bunte, K, Dochnal, R, Hervas, A, Stadler, C, Fernández-Rivas, A, Fairchild, G, Popma, A, Dikeos, D, Konrad, K, Herpertz-Dahlmann, B, Freitag, CM, Rotshtein, P & De Brito, S 2020, 'Positive and negative parenting in conduct disorder with high versus low levels of callous-unemotional traits', Development and Psychopathology, pp. 1-12. https://doi.org/10.1017/S0954579420000279
Chong, SY, Tino, P & He, J 2019, 'Coevolutionary systems and PageRank', Artificial Intelligence, vol. 277, 103164. https://doi.org/10.1016/j.artint.2019.103164
Conference contribution
Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Improving sampling in evolution strategies through mixture-based distributions built from past problem instances. in Parallel Problem Solving from Nature – PPSN XVI. Lecture Notes in Computer Science, vol. 12269, Springer, pp. 583-596, Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020., Leiden, Netherlands, 5/09/20. https://doi.org/10.1007/978-3-030-58112-1_40
Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Representing experience in continuous evolutionary optimisation through problem-tailored search operators. in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 1-7, 2020 IEEE Congress on Evolutionary Computation (IEE CEC 2020), Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/CEC48606.2020.9185687
Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2019, Learning Transferable Variation Operators in a Continuous Genetic Algorithm. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)., 9002976, Institute of Electrical and Electronics Engineers (IEEE), pp. 2027-2033, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002976
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